path_proj = here::here()
path_source = file.path(path_proj, "source")
source(file.path(path_source, "simulation", "simulations_functions_final.R"))
##
## Attaching package: 'tidyr'
## The following object is masked from 'package:reshape2':
##
## smiths
source(file.path(path_source, "functions", "plot_function.R"))
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
source(file.path(path_source, "functions", "fit_function.R"))
source(file.path(path_source, "functions", "table_function.R"))
# place for draws
# mac
posterior_draws_path = file.path(Sys.getenv("HOME"), "Desktop", "draws", "testEach")
#Windows
#posterior_draws_path = file.path(Sys.getenv("USERPROFILE"), "Desktop", "draws", "testEach")
#data path
data_save_path = file.path(path_proj, "data", "fitted_model", "simulation", "4. b_ou")
#models
q_constant <- file.path(path_proj, "source", "models",
"q-constant.stan")
b_constant <- file.path(path_proj, "source", "models",
"b-constant.stan")
b_rw <- file.path(path_proj, "source", "models",
"b-rw1.stan")
b_ou <- file.path(path_proj, "source", "models",
"b-ou.stan")
compiled_models <- list(
q_constant = cmdstan_model(q_constant),
b_constant = cmdstan_model(b_constant),
b_rw = cmdstan_model(b_rw),
b_ou = cmdstan_model(b_ou)
)
models_to_use <- c("q_constant", "b_constant", "b_rw", "b_ou")
simulate data - FR
setting
###### setting #####
seed <- 123
set.seed(seed)
# data
alpha_increase_seq_1 <- seq(10, 750, by = 30)
alpha_decrease_seq_1 <- seq(750, 10, by = -30)
alpha_lamb = c( rep(10,5), alpha_increase_seq_1 + rnorm(alpha_increase_seq_1,10,10),
alpha_decrease_seq_1 + rnorm(alpha_decrease_seq_1,10,10),
rep(10,5))
beta_lamb = 0.5
T = 60
# reprot delay
D <- 15;
# Time period for checking
D_check <- 5
first_date <- as.Date("2024-01-01")
scoreRange <- c(first_date+days(9), first_date+days(19), first_date+days(29),
first_date+days(39), first_date+days(49))
params_b_ou_FR <- list(
data = list(
alpha_lamb = alpha_lamb,
beta_lamb = beta_lamb,
T = T,
date_start = as.Date("2024-01-01"),
D = D
),
q_model = list(
method = "b_ou",
method_params = list(theta_logb = 0.3, mu_logb = log(0.7), init_logb = log(0.7), sigma_logb = 0.2,
theta_logitphi = 0.3, mu_logitphi = 1, init_logitphi = 1, sigma_logitphi = 0.2)
)
)
b_ou_FR <- simulateData(params_b_ou_FR)
## [1] "here we go"
par(mfrow = c(2, 1))
plot(b_ou_FR$b, pch = 19, type = "b")
plot(b_ou_FR$phi, pch = 19, type = "b")
par(mfrow = c(1, 1))
matplot(t(b_ou_FR$q), type = "l", lty = 1, ylim = c(0, 1))
# ou_NFR
params_b_ou_NFR <- list(
data = list(
alpha_lamb = alpha_lamb,
beta_lamb = beta_lamb,
T = T,
date_start = as.Date("2024-01-01"),
D = D
),
q_model = list(
method = "b_ou",
method_params = list(theta_logb = 0.2, mu_logb = log(0.1), init_logb = log(0.1), sigma_logb = 0.15,
theta_logitphi = 0.2, mu_logitphi = 1.5, init_logitphi = 1.5, sigma_logitphi = 0.15)
)
)
b_ou_NFR <- simulateData(params_b_ou_NFR)
## [1] "here we go"
par(mfrow = c(2, 1))
plot(b_ou_NFR$b, pch = 19, type = "b")
plot(b_ou_NFR$phi, pch = 19, type = "b")
par(mfrow = c(1, 1))
matplot(t(b_ou_NFR$q), type = "l", lty = 1, ylim = c(0, 1))
expplot
# exploritary analysis
page_num <- ceiling(nrow(b_ou_FR$case_reported_cumulated)/16)
exp_plot_ou <- fit_exp_plot(b_ou_FR$case_reported_cumulated,ncol = 4, nrow = 4, page = c(1:page_num), if_fit = T)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
print(exp_plot_ou)
## $plots
## $plots[[1]]
##
## $plots[[2]]
##
## $plots[[3]]
##
## $plots[[4]]
##
##
## $coefficients
## b phi
## 1 1.0184186 0.253431592
## 2 0.6509694 0.546903210
## 3 0.7644895 0.136673976
## 4 1.2869640 0.203627561
## 5 0.3912118 0.300133052
## 6 0.7621946 0.007533969
## 7 0.6660028 0.171683278
## 8 0.5773877 0.254000196
## 9 0.4946644 0.268279415
## 10 0.5883842 0.239827799
## 11 0.7630322 0.202700208
## 12 0.6623841 0.304778809
## 13 0.9003512 0.321959789
## 14 0.6869329 0.280528247
## 15 0.6180896 0.314059378
## 16 0.6570050 0.316283697
## 17 0.6832462 0.268327876
## 18 0.7039898 0.258409132
## 19 0.6313040 0.252188622
## 20 0.8399478 0.240887664
## 21 0.7790189 0.198963504
## 22 0.8458926 0.198780186
## 23 0.8819860 0.236302753
## 24 1.0356185 0.272928726
## 25 1.4930619 0.213407152
## 26 1.0831836 0.271403455
## 27 0.8463732 0.250994725
## 28 0.8031341 0.260904728
## 29 0.6290154 0.284322147
## 30 0.5291156 0.333024875
## 31 0.5792335 0.284138676
## 32 0.5263670 0.253988563
## 33 0.4082156 0.272677492
## 34 0.5469907 0.268459929
## 35 0.5964300 0.272924992
## 36 0.5389308 0.311407775
## 37 0.5722262 0.347599713
## 38 0.5731940 0.333035066
## 39 0.8474409 0.362714288
## 40 0.8816275 0.302867356
## 41 0.6565743 0.316318277
## 42 0.8850903 0.283031911
## 43 0.8945391 0.265147315
## 44 0.5466306 0.233898490
## 45 0.4094268 0.220531416
## 46 0.7308413 0.306687247
## 47 0.8409546 0.305236260
## 48 0.5549634 0.334415079
## 49 0.5547306 0.357562807
## 50 0.3802698 0.281724332
## 51 0.5995839 0.325938598
## 52 0.8741929 0.291401039
## 53 0.7899340 0.326956538
## 54 0.7464737 0.338130582
## 55 0.9055233 0.343743089
## 56 1.1627079 0.426495394
## 57 1.3283720 0.441025876
## 58 0.8215360 0.055076447
## 59 0.6494544 0.294071003
## 60 0.9061664 -0.039585126
exp_plot_ou$coefficients
## b phi
## 1 1.0184186 0.253431592
## 2 0.6509694 0.546903210
## 3 0.7644895 0.136673976
## 4 1.2869640 0.203627561
## 5 0.3912118 0.300133052
## 6 0.7621946 0.007533969
## 7 0.6660028 0.171683278
## 8 0.5773877 0.254000196
## 9 0.4946644 0.268279415
## 10 0.5883842 0.239827799
## 11 0.7630322 0.202700208
## 12 0.6623841 0.304778809
## 13 0.9003512 0.321959789
## 14 0.6869329 0.280528247
## 15 0.6180896 0.314059378
## 16 0.6570050 0.316283697
## 17 0.6832462 0.268327876
## 18 0.7039898 0.258409132
## 19 0.6313040 0.252188622
## 20 0.8399478 0.240887664
## 21 0.7790189 0.198963504
## 22 0.8458926 0.198780186
## 23 0.8819860 0.236302753
## 24 1.0356185 0.272928726
## 25 1.4930619 0.213407152
## 26 1.0831836 0.271403455
## 27 0.8463732 0.250994725
## 28 0.8031341 0.260904728
## 29 0.6290154 0.284322147
## 30 0.5291156 0.333024875
## 31 0.5792335 0.284138676
## 32 0.5263670 0.253988563
## 33 0.4082156 0.272677492
## 34 0.5469907 0.268459929
## 35 0.5964300 0.272924992
## 36 0.5389308 0.311407775
## 37 0.5722262 0.347599713
## 38 0.5731940 0.333035066
## 39 0.8474409 0.362714288
## 40 0.8816275 0.302867356
## 41 0.6565743 0.316318277
## 42 0.8850903 0.283031911
## 43 0.8945391 0.265147315
## 44 0.5466306 0.233898490
## 45 0.4094268 0.220531416
## 46 0.7308413 0.306687247
## 47 0.8409546 0.305236260
## 48 0.5549634 0.334415079
## 49 0.5547306 0.357562807
## 50 0.3802698 0.281724332
## 51 0.5995839 0.325938598
## 52 0.8741929 0.291401039
## 53 0.7899340 0.326956538
## 54 0.7464737 0.338130582
## 55 0.9055233 0.343743089
## 56 1.1627079 0.426495394
## 57 1.3283720 0.441025876
## 58 0.8215360 0.055076447
## 59 0.6494544 0.294071003
## 60 0.9061664 -0.039585126
exp_b_data_ou<- data.frame( date = as.Date(rownames(b_ou_FR$case_reported_cumulated)),
b = exp_plot_ou$coefficients$b)
exp_b_plot_ou <- ggplot(exp_b_data_ou, aes(x = date, y = b)) +
geom_point(color = "black", size = 1.5) +
geom_smooth(method = "loess", se = TRUE,
color = "blue", fill = "grey", alpha = 0.5) +
theme_minimal() +
labs(x = NULL, y = "Y", title = "Smoothed Curve of parameter b")
print(exp_b_plot_ou)
## `geom_smooth()` using formula = 'y ~ x'
# exploritary analysis
page_num <- ceiling(nrow(b_ou_NFR$case_reported_cumulated)/16)
exp_plot_ou <- fit_exp_plot(b_ou_NFR$case_reported_cumulated,ncol = 4, nrow = 4, page = c(1:page_num), if_fit = T)
print(exp_plot_ou)
## $plots
## $plots[[1]]
##
## $plots[[2]]
##
## $plots[[3]]
##
## $plots[[4]]
##
##
## $coefficients
## b phi
## 1 0.1607346 0.08526495
## 2 0.1387379 0.39334920
## 3 0.1881441 0.30373372
## 4 0.1005666 0.23516450
## 5 0.1931866 0.05402949
## 6 0.1922913 0.48818141
## 7 0.1638292 0.17874300
## 8 0.1546258 0.22823144
## 9 0.1463539 0.25607718
## 10 0.1533422 0.24070483
## 11 0.1691421 0.23803313
## 12 0.1565306 0.19818969
## 13 0.1556989 0.22650387
## 14 0.1620785 0.18961150
## 15 0.1713553 0.21191501
## 16 0.1803743 0.14048080
## 17 0.1692417 0.17785194
## 18 0.1847041 0.10323804
## 19 0.1889704 0.09742637
## 20 0.1676988 0.11164840
## 21 0.1624350 0.09863313
## 22 0.1811429 0.08812489
## 23 0.1678583 0.13704453
## 24 0.1649921 0.11801358
## 25 0.1659259 0.11456425
## 26 0.1636334 0.15802306
## 27 0.1595850 0.14412192
## 28 0.1633322 0.19151782
## 29 0.1601417 0.19807313
## 30 0.1761062 0.21671626
## 31 0.1720761 0.17107231
## 32 0.1948724 0.20224538
## 33 0.1624139 0.16540786
## 34 0.1649930 0.14688458
## 35 0.1687533 0.19983575
## 36 0.1853506 0.14074984
## 37 0.1802496 0.20171508
## 38 0.1746101 0.21637612
## 39 0.1834760 0.16829431
## 40 0.1660187 0.18218919
## 41 0.1955013 0.16014742
## 42 0.1834835 0.12953189
## 43 0.2307871 0.20862896
## 44 0.1635083 0.16687631
## 45 0.1733182 0.16030699
## 46 0.1721403 0.17139965
## 47 0.1729932 0.12502694
## 48 0.1607783 0.18134152
## 49 0.1737043 0.13383785
## 50 0.1866806 0.15535871
## 51 0.1770962 0.13958139
## 52 0.1622209 0.19588355
## 53 0.1645726 0.08569237
## 54 0.1476060 0.22149815
## 55 0.1858383 0.22147106
## 56 0.1692664 0.21831461
## 57 0.2532632 0.27274666
## 58 0.2076270 0.15474529
## 59 0.1582585 -0.10456069
## 60 0.1893347 0.03540411
exp_plot_ou$coefficients
## b phi
## 1 0.1607346 0.08526495
## 2 0.1387379 0.39334920
## 3 0.1881441 0.30373372
## 4 0.1005666 0.23516450
## 5 0.1931866 0.05402949
## 6 0.1922913 0.48818141
## 7 0.1638292 0.17874300
## 8 0.1546258 0.22823144
## 9 0.1463539 0.25607718
## 10 0.1533422 0.24070483
## 11 0.1691421 0.23803313
## 12 0.1565306 0.19818969
## 13 0.1556989 0.22650387
## 14 0.1620785 0.18961150
## 15 0.1713553 0.21191501
## 16 0.1803743 0.14048080
## 17 0.1692417 0.17785194
## 18 0.1847041 0.10323804
## 19 0.1889704 0.09742637
## 20 0.1676988 0.11164840
## 21 0.1624350 0.09863313
## 22 0.1811429 0.08812489
## 23 0.1678583 0.13704453
## 24 0.1649921 0.11801358
## 25 0.1659259 0.11456425
## 26 0.1636334 0.15802306
## 27 0.1595850 0.14412192
## 28 0.1633322 0.19151782
## 29 0.1601417 0.19807313
## 30 0.1761062 0.21671626
## 31 0.1720761 0.17107231
## 32 0.1948724 0.20224538
## 33 0.1624139 0.16540786
## 34 0.1649930 0.14688458
## 35 0.1687533 0.19983575
## 36 0.1853506 0.14074984
## 37 0.1802496 0.20171508
## 38 0.1746101 0.21637612
## 39 0.1834760 0.16829431
## 40 0.1660187 0.18218919
## 41 0.1955013 0.16014742
## 42 0.1834835 0.12953189
## 43 0.2307871 0.20862896
## 44 0.1635083 0.16687631
## 45 0.1733182 0.16030699
## 46 0.1721403 0.17139965
## 47 0.1729932 0.12502694
## 48 0.1607783 0.18134152
## 49 0.1737043 0.13383785
## 50 0.1866806 0.15535871
## 51 0.1770962 0.13958139
## 52 0.1622209 0.19588355
## 53 0.1645726 0.08569237
## 54 0.1476060 0.22149815
## 55 0.1858383 0.22147106
## 56 0.1692664 0.21831461
## 57 0.2532632 0.27274666
## 58 0.2076270 0.15474529
## 59 0.1582585 -0.10456069
## 60 0.1893347 0.03540411
exp_b_data_ou<- data.frame( date = as.Date(rownames(b_ou_NFR$case_reported_cumulated)),
b = exp_plot_ou$coefficients$b)
exp_b_plot_ou <- ggplot(exp_b_data_ou, aes(x = date, y = b)) +
geom_point(color = "black", size = 1.5) +
geom_smooth(method = "loess", se = TRUE,
color = "blue", fill = "grey", alpha = 0.5) +
theme_minimal() +
labs(x = NULL, y = "Y", title = "Smoothed Curve of parameter b")
print(exp_b_plot_ou)
## `geom_smooth()` using formula = 'y ~ x'
fit model
out_b_ou_FR <-
nowcasting_moving_window(b_ou_FR$case_reported_cumulated, scoreRange = scoreRange,
case_true = b_ou_FR$case_true,
start_date = as.Date("2024-01-01"),
D = D, seeds = seed,
methods =models_to_use,
compiled_models = compiled_models,
iter_sampling = 2000, iter_warmup = 1000, refresh = 0,
num_chains = 3, suppress_output = T,
posterior_draws_path = file.path(posterior_draws_path, "b_ou")
)
## ====================
## now=2024-01-10 (1/5)
## ====================
## Warning in nowcasting_moving_window(b_ou_FR$case_reported_cumulated, scoreRange
## = scoreRange, : The number of rows of the input data is smaller than number of
## max delay D, which might cause inaccuracy.
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.7 seconds.
## Chain 2 finished in 0.6 seconds.
## Chain 3 finished in 0.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.6 seconds.
## Total execution time: 2.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.2 seconds.
## Chain 2 finished in 0.2 seconds.
## Chain 3 finished in 0.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.2 seconds.
## Total execution time: 1.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.4 seconds.
## Chain 2 finished in 2.7 seconds.
## Chain 3 finished in 1.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.3 seconds.
## Total execution time: 7.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.2 seconds.
## Chain 2 finished in 3.0 seconds.
## Chain 3 finished in 1.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.5 seconds.
## Total execution time: 7.8 seconds.
##
## ====================
## now=2024-01-20 (2/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.6 seconds.
## Chain 2 finished in 1.4 seconds.
## Chain 3 finished in 1.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.5 seconds.
## Total execution time: 4.9 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.4 seconds.
## Chain 2 finished in 0.4 seconds.
## Chain 3 finished in 0.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.4 seconds.
## Total execution time: 1.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.5 seconds.
## Chain 2 finished in 3.3 seconds.
## Chain 3 finished in 3.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.4 seconds.
## Total execution time: 10.6 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.3 seconds.
## Chain 2 finished in 5.4 seconds.
## Chain 3 finished in 3.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.9 seconds.
## Total execution time: 12.1 seconds.
##
## ====================
## now=2024-01-30 (3/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.2 seconds.
## Chain 2 finished in 2.0 seconds.
## Chain 3 finished in 1.9 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.0 seconds.
## Total execution time: 6.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.6 seconds.
## Chain 2 finished in 0.6 seconds.
## Chain 3 finished in 0.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.6 seconds.
## Total execution time: 2.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 4.3 seconds.
## Chain 2 finished in 4.3 seconds.
## Chain 3 finished in 4.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.4 seconds.
## Total execution time: 13.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.5 seconds.
## Chain 2 finished in 3.9 seconds.
## Chain 3 finished in 3.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.6 seconds.
## Total execution time: 11.1 seconds.
##
## ====================
## now=2024-02-09 (4/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.6 seconds.
## Chain 2 finished in 2.7 seconds.
## Chain 3 finished in 2.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.7 seconds.
## Total execution time: 8.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.8 seconds.
## Chain 2 finished in 0.8 seconds.
## Chain 3 finished in 0.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.8 seconds.
## Total execution time: 2.6 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 5.2 seconds.
## Chain 2 finished in 6.4 seconds.
## Chain 3 finished in 6.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 6.1 seconds.
## Total execution time: 18.5 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 5.3 seconds.
## Chain 2 finished in 6.0 seconds.
## Chain 3 finished in 5.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 5.6 seconds.
## Total execution time: 17.0 seconds.
##
## ====================
## now=2024-02-19 (5/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.6 seconds.
## Chain 2 finished in 3.5 seconds.
## Chain 3 finished in 3.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 3.6 seconds.
## Total execution time: 11.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.1 seconds.
## Chain 2 finished in 1.0 seconds.
## Chain 3 finished in 1.0 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.0 seconds.
## Total execution time: 3.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 5.9 seconds.
## Chain 2 finished in 7.7 seconds.
## Chain 3 finished in 6.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 6.6 seconds.
## Total execution time: 20.0 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 6.9 seconds.
## Chain 2 finished in 5.8 seconds.
## Chain 3 finished in 6.0 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 6.2 seconds.
## Total execution time: 19.2 seconds.
save(out_b_ou_FR, file = file.path(data_save_path, "FR_b_ou.RData"))
#load(file.path(data_save_path, "FR_b_ou.RData"))
results_b_ou_FR <- nowcasts_table(out_b_ou_FR, D = D, report_unit = "day",
methods = models_to_use)
plots_b_ou_FR <- nowcasts_plot(results_b_ou_FR, D = D, report_unit = "day", methods = models_to_use)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
for (i in 1:length(results_b_ou_FR)) {
print(calculate_metrics(results_b_ou_FR[[i]],
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 62.94 59.77 43.77 59.08 106.61 0.7 q_constant
## 2 15.73 9.25 9.35 8.04 50.00 1.0 b_constant
## 3 16.49 8.63 8.98 7.17 62.40 1.0 b_rw
## 4 17.59 9.89 10.22 8.49 63.10 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 53.88 12.68 39.27 12.00 104.11 0.65 q_constant
## 2 29.59 4.02 11.49 2.55 77.56 1.00 b_constant
## 3 31.99 4.19 13.48 2.64 96.11 1.00 b_rw
## 4 26.08 3.40 9.76 2.00 93.46 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 110.57 8.21 47.47 4.94 110.07 0.87 q_constant
## 2 87.04 6.23 28.23 2.95 103.51 0.90 b_constant
## 3 36.53 2.74 14.45 1.53 135.61 1.00 b_rw
## 4 51.51 3.61 14.88 1.52 135.18 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 48.02 4.24 24.17 2.48 112.53 0.95 q_constant
## 2 48.38 4.18 24.53 2.54 110.31 0.95 b_constant
## 3 29.25 2.78 9.62 1.18 138.78 1.00 b_rw
## 4 31.83 3.00 9.20 1.14 139.44 1.00 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 26.15 3.47 17.15 2.40 108.01 0.98 q_constant
## 2 25.02 3.28 15.20 2.10 106.35 0.98 b_constant
## 3 17.36 3.33 7.72 1.51 115.93 0.98 b_rw
## 4 14.54 2.68 6.42 1.26 114.83 1.00 b_ou
for (i in 1:length(results_b_ou_FR)) {
print(calculate_metrics(data.table::last(results_b_ou_FR[[i]],D_check),
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 88.34 53.64 77.03 53.49 185.02 0.4 q_constant
## 2 22.22 11.23 17.83 10.96 82.61 1.0 b_constant
## 3 23.30 10.74 17.21 9.87 108.01 1.0 b_rw
## 4 24.84 12.02 19.49 11.47 109.20 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 80.13 11.96 72.25 10.52 217.63 0.4 q_constant
## 2 58.33 6.90 36.94 4.46 160.20 1.0 b_constant
## 3 63.35 7.51 45.26 5.52 231.62 1.0 b_rw
## 4 51.73 6.11 32.31 3.85 220.82 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 255.64 17.62 175.11 12.47 244.02 0.6 q_constant
## 2 208.02 14.21 121.62 8.53 228.02 0.6 b_constant
## 3 88.70 6.15 72.48 5.13 416.23 1.0 b_rw
## 4 125.84 8.56 76.90 5.32 413.24 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 106.92 9.85 75.00 6.84 197.61 0.8 q_constant
## 2 101.09 9.28 74.16 6.74 190.42 0.8 b_constant
## 3 79.97 7.37 54.59 4.98 385.43 1.0 b_rw
## 4 87.98 8.08 52.54 4.76 389.63 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 26.02 5.11 22.22 4.39 126.60 1 q_constant
## 2 21.26 4.19 16.71 3.28 124.83 1 b_constant
## 3 42.47 8.93 36.21 7.40 199.22 1 b_rw
## 4 32.44 6.66 28.06 5.67 187.41 1 b_ou
print(plots_b_ou_FR)
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## [[5]]
out_b_ou_NFR <-
nowcasting_moving_window(b_ou_NFR$case_reported_cumulated, scoreRange = scoreRange,
case_true = b_ou_NFR$case_true,
start_date = as.Date("2024-01-01"),
D = D, seeds = seed,
methods =models_to_use,
compiled_models = compiled_models,
iter_sampling = 2000, iter_warmup = 1000, refresh = 0,
num_chains = 3, suppress_output = T,
posterior_draws_path = file.path(posterior_draws_path, "b_ou")
)
## ====================
## now=2024-01-10 (1/5)
## ====================
## Warning in nowcasting_moving_window(b_ou_NFR$case_reported_cumulated,
## scoreRange = scoreRange, : The number of rows of the input data is smaller than
## number of max delay D, which might cause inaccuracy.
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.7 seconds.
## Chain 2 finished in 0.7 seconds.
## Chain 3 finished in 0.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.7 seconds.
## Total execution time: 2.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.3 seconds.
## Chain 2 finished in 0.2 seconds.
## Chain 3 finished in 0.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.3 seconds.
## Total execution time: 1.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.2 seconds.
## Chain 2 finished in 2.1 seconds.
## Chain 3 finished in 2.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.7 seconds.
## Total execution time: 8.3 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.2 seconds.
## Chain 2 finished in 3.9 seconds.
## Chain 3 finished in 2.6 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.9 seconds.
## Total execution time: 9.1 seconds.
##
## ====================
## now=2024-01-20 (2/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.4 seconds.
## Chain 2 finished in 1.5 seconds.
## Chain 3 finished in 1.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.4 seconds.
## Total execution time: 4.7 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 0.9 seconds.
## Chain 2 finished in 0.8 seconds.
## Chain 3 finished in 0.9 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 0.9 seconds.
## Total execution time: 2.9 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 5.6 seconds.
## Chain 2 finished in 5.2 seconds.
## Chain 3 finished in 6.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 5.6 seconds.
## Total execution time: 17.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 3.9 seconds.
## Chain 2 finished in 4.8 seconds.
## Chain 3 finished in 5.2 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 4.6 seconds.
## Total execution time: 14.1 seconds.
##
## ====================
## now=2024-01-30 (3/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.6 seconds.
## Chain 2 finished in 1.7 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.7 seconds.
## Total execution time: 5.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.1 seconds.
## Chain 2 finished in 1.1 seconds.
## Chain 3 finished in 1.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.1 seconds.
## Total execution time: 3.6 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 9.3 seconds.
## Chain 2 finished in 8.9 seconds.
## Chain 3 finished in 11.1 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 9.8 seconds.
## Total execution time: 29.6 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 6.5 seconds.
## Chain 2 finished in 8.1 seconds.
## Chain 3 finished in 6.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 7.1 seconds.
## Total execution time: 21.8 seconds.
##
## ====================
## now=2024-02-09 (4/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.1 seconds.
## Chain 2 finished in 1.9 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.9 seconds.
## Total execution time: 6.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.7 seconds.
## Chain 2 finished in 1.5 seconds.
## Chain 3 finished in 1.8 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.7 seconds.
## Total execution time: 5.4 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 13.5 seconds.
## Chain 2 finished in 12.9 seconds.
## Chain 3 finished in 14.4 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 13.6 seconds.
## Total execution time: 41.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 12.7 seconds.
## Chain 2 finished in 10.9 seconds.
## Chain 3 finished in 11.3 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 11.6 seconds.
## Total execution time: 35.3 seconds.
##
## ====================
## now=2024-02-19 (5/5)
## ====================
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 2.1 seconds.
## Chain 2 finished in 2.5 seconds.
## Chain 3 finished in 2.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 2.4 seconds.
## Total execution time: 7.6 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 1.6 seconds.
## Chain 2 finished in 1.6 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 1.6 seconds.
## Total execution time: 5.2 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 13.3 seconds.
## Chain 2 finished in 15.8 seconds.
## Chain 3 finished in 17.7 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 15.6 seconds.
## Total execution time: 47.1 seconds.
##
## Running MCMC with 3 sequential chains...
##
## Chain 1 finished in 14.9 seconds.
## Chain 2 finished in 14.0 seconds.
## Chain 3 finished in 11.5 seconds.
##
## All 3 chains finished successfully.
## Mean chain execution time: 13.5 seconds.
## Total execution time: 40.7 seconds.
save(out_b_ou_NFR, file = file.path(data_save_path, "NFR_b_ou.RData"))
# load(file.path(data_save_path, "NFR_b_ou.RData"))
results_b_ou_NFR <- nowcasts_table(out_b_ou_NFR, D = D, report_unit = "day",
methods = models_to_use)
plots_b_ou_NFR <- nowcasts_plot(results_b_ou_NFR, D = D, report_unit = "day", methods = models_to_use)
for (i in 1:length(results_b_ou_NFR)) {
print(calculate_metrics(results_b_ou_NFR[[i]],
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 18.24 21.00 13.27 18.51 82.00 1.0 q_constant
## 2 67.66 49.19 45.39 47.61 41.30 0.5 b_constant
## 3 70.83 50.09 47.19 48.45 46.30 0.5 b_rw
## 4 69.75 50.11 46.65 48.65 44.81 0.4 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 156.22 27.56 104.51 25.22 90.06 0.35 q_constant
## 2 159.02 27.83 106.85 25.26 88.65 0.35 b_constant
## 3 161.04 24.68 102.16 20.83 152.01 0.60 b_rw
## 4 156.40 25.17 101.42 21.88 144.82 0.65 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 162.84 22.83 125.23 21.13 103.78 0.23 q_constant
## 2 109.16 13.61 66.98 11.01 120.85 0.57 b_constant
## 3 80.59 11.18 53.03 9.20 254.67 0.90 b_rw
## 4 71.68 11.60 48.32 9.22 209.59 0.87 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 151.22 18.90 118.55 16.84 109.21 0.25 q_constant
## 2 102.75 10.97 67.18 9.18 132.96 0.65 b_constant
## 3 68.73 9.38 46.54 7.33 233.12 0.92 b_rw
## 4 57.76 9.24 41.09 7.06 223.87 0.95 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 153.79 19.81 127.67 18.25 101.63 0.16 q_constant
## 2 77.65 10.64 54.51 8.37 115.57 0.70 b_constant
## 3 62.07 11.23 45.61 8.27 171.99 0.90 b_rw
## 4 60.71 10.59 41.41 7.62 174.75 0.88 b_ou
for (i in 1:length(results_b_ou_NFR)) {
print(calculate_metrics(data.table::last(results_b_ou_NFR[[i]],D_check),
methods = models_to_use))
}
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 25.59 22.50 23.80 20.53 140.80 1.0 q_constant
## 2 95.29 51.40 82.63 50.01 68.20 0.2 b_constant
## 3 99.79 53.35 86.27 52.00 77.80 0.2 b_rw
## 4 98.26 52.82 85.08 51.64 75.41 0.2 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 291.15 36.46 274.34 35.18 180.82 0.0 q_constant
## 2 295.47 37.03 278.81 35.78 177.61 0.0 b_constant
## 3 307.80 38.99 297.00 38.35 344.63 0.0 b_rw
## 4 296.19 37.33 283.66 36.52 328.44 0.2 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 196.89 15.74 184.28 14.41 247.01 0.2 q_constant
## 2 180.31 12.68 128.24 9.46 292.42 0.4 b_constant
## 3 126.37 9.56 108.97 8.35 846.54 1.0 b_rw
## 4 111.42 8.33 91.85 7.01 614.27 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 97.43 8.65 80.56 7.26 209.21 0.6 q_constant
## 2 183.11 16.11 164.75 14.67 257.61 0.6 b_constant
## 3 103.42 9.91 82.01 7.72 625.44 1.0 b_rw
## 4 72.65 6.54 65.51 5.96 548.24 1.0 b_ou
## RMSE RMSPE MAE MAPE Interval_Width Coverage_Rate Method
## 1 113.30 23.96 109.87 23.12 122.61 0.2 q_constant
## 2 51.78 11.16 42.99 9.18 143.01 0.6 b_constant
## 3 105.47 22.88 101.57 21.75 259.82 0.8 b_rw
## 4 68.27 15.08 59.52 13.09 238.20 0.8 b_ou
print(plots_b_ou_NFR)
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